Based on the observations that Farthest Point Sampling (FPS) provides a simple and intuitive technique to select points covering the point cloud structure [1]}, we built a sampling module on top of this sampling strategy, where points are sampled from a high dimensional space instead of the input xyz-space. Although any clustering algorithm could be adequate, we utilized FPS module since it covers sufficiently the input space without solving any optimization problem. Intuitively, using this formulation we are allowed to interfere the selection process and transform it to a learnable module. The revised sampling module will select point embeddings that cover the perceptual latent space, enabling the preservation of both the shape and the features of the input.
